hardMultiple Choice
PMLE Practice Question: The pipeline fails during the evaluate component…
Network Topology
The pipeline fails during the evaluate component with error "Model not found". What is the most likely cause?
⚠ Common exam trap
Google Cloud often tests the distinction between resource resolution errors (like 'Model not found') and data/validation errors, tricking candidates into confusing dataset or threshold issues with pipeline step output references.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model_id parameter is referencing the wrong output
The error 'Model not found' during the evaluate component indicates that the model_id parameter is referencing an output that does not exist or is incorrectly named. In Vertex AI Pipelines, the evaluate component takes the model artifact from a previous training step via an output parameter or artifact reference. If the model_id parameter points to a wrong output (e.g., a different step's output or a misspelled reference), the pipeline cannot locate the model. This is the most likely cause because the error is specific to model resolution, not dataset or threshold issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The dataset_id is misspelled
Why it's wrong here
A misspelled dataset_id would fail during data loading or training, not at the evaluate component's model lookup. It is tempting because identifier typos are a frequent pipeline error, and it would be correct if the failure occurred while reading the input dataset rather than resolving a model.
- ✓
The model_id parameter is referencing the wrong output
Why this is correct
The evaluate component resolves its input model via the model_id parameter, which must point to the trained model output produced by an earlier pipeline step. Referencing the wrong output leaves no artefact at that path, so evaluation fails with "Model not found".
- ✗
The training container did not produce a model artifact
Why it's wrong here
A training container that produced no artifact would leave the evaluate component with nothing to load, matching the error. It is tempting because a missing output is a common pipeline failure, but the stem attributes the fault to evaluate, and a training-side omission would typically surface earlier in the run.
- ✗
The threshold value is invalid
Why it's wrong here
A threshold value governs pass/fail scoring, not model resolution, so it cannot produce a missing-model error; the evaluate component fails because the registered model name or version does not match the workspace. Thresholds are configured when you want to gate deployment on a metric.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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